Visual based autonomous defect/change detection using unmanned vehicles is in its infancy. Given details about an object using visual input and using an unmanned vehicle to assess and detect change in the object of interest in currently unfeasible. Due to limited computational and communication resources on the unmanned vehicle, automatic change detection is non-trivial. A new paradigm of visual change detection methods are required that can work in real time.
Visual change detection is an extremely challenging task and it involves significant challenges due to variation in illumination, lighting and other environmental conditions. In traditional saliency, features are captured based on well-known image attributes such as color, orientation, texture, motion etc. However, in complex scenarios these attributes may have different weights. For fast computation during navigation, the regions of interest should be narrowed down based on certain fixed criterion. One such criterion could be ontology of the domain that will force the weight re-adjustment and quickly focuses on the regions of interest. Current saliency detection systems are not controlled using ontology or semantics.